Proteins populate structural ensembles. Defining these ensembles and understanding the role of the interconversions between structures is a grand challenge of structural biology. My work addresses that challenge through the development and application of new methods to reveal sparsely populated structures. Quantitative electron-density map interpretation, implemented in Ringer, provides an objective, systematic method to identify previously undiscovered alternate side chain substates that mediate conformational transitions in proteins.
Next, I applied these methods to study the role of the interconversions of an enzyme, the human proline isomerase CypA, between two conformations during its catalytic cycle. Using the dual strategies of ambient-temperature X-ray crystallographic data collection and automated electron-density sampling, I defined the previously undiscovered minor state as a network of alternate side chain conformations. A conservative mutation outside the active site inverts the equilibrium between the substates and causes large, parallel reductions in the conformational interconversion rates and the catalytic rate.
The temperature dependent differences in electron density observed with CypA led me to critically examine the assumption that crystal freezing does not significantly bias protein structure. I found extensive remodeling of the crystal lattice upon freezing. Crystal freezing also leads to improved packing through reduction of small voids and a reduction in protein volume. I used real-space electron density sampling to show that these voids can be transiently populated by alternate conformations in the room temperature ensemble. This work shows how crystal freezing biases our understanding of protein packing and can lead to differences in the spatial distribution of the dynamic features of protein side chains.
These studies highlight the importance of conformational diversity in protein function. By looking beyond the model and into the map, we can find that polysteric regions often populate conformations that resemble the structures populated along reaction or evolutionary trajectories. Thus, understanding polysterism yields insights into where a protein might visit during its reaction cycle and where it has been during its evolution.
|Commitee:||Eisen, Michael, Kuriyan, John, Wemmer, David|
|School:||University of California, Berkeley|
|Department:||Molecular & Cell Biology|
|School Location:||United States -- California|
|Source:||DAI-B 74/07(E), Dissertation Abstracts International|
|Subjects:||Molecular biology, Bioinformatics, Biophysics|
|Keywords:||Enzymology, Nuclear magnetic resonance spectroscopy, Polysterism, Protein dynamics, Protein structure, X-ray crystallography|
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